diff --git a/notebooks/results_all_models.ipynb b/notebooks/results_all_models.ipynb new file mode 100644 index 0000000..4c27fe1 --- /dev/null +++ b/notebooks/results_all_models.ipynb @@ -0,0 +1,1889 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [], + "source": [ + "top_n_pred = [1,2,3]\n", + "models = [\"load_m1\", \"load_m2\", \"load_m3\"]\n", + "datasets = [\"1_complete\", \"2_cf_cr_optional\", \"3_cp_cf_cr_optional\", \"4_complete_without_return_expressions\",'5_short_dim']\n", + "n_repetitions = 3\n", + "shortners = ['','42_']\n", + "output_dir=\"../output/reports/json/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_avg_dict(result_list_dict):\n", + " return {\n", + " 'precision':sum(d['precision'] for d in result_list_dict) / len(result_list_dict),\n", + " 'recall':sum(d['recall'] for d in result_list_dict) / len(result_list_dict),\n", + " 'f1-score':sum(d['f1-score'] for d in result_list_dict) / len(result_list_dict),\n", + " 'support':sum(d['support'] for d in result_list_dict) / len(result_list_dict),\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "metadata": {}, + "outputs": [], + "source": [ + "results_unfiltered = dict()\n", + "\n", + "index = pd.MultiIndex.from_product([models, datasets],\n", + " names=['model', 'subtype'])\n", + "\n", + "columns = pd.MultiIndex.from_product([top_n_pred, ['precision', 'recall', 'f1-score']],\n", + " names=['top', 'stats'])\n", + "\n", + "df_macro_avg_unfiltered = pd.DataFrame(index=index, columns=columns)\n", + "df_weighted_avg_unfiltered = pd.DataFrame(index=index, columns=columns)\n", + "\n", + "for model in models:\n", + " for dataset in datasets:\n", + " for n_rep in range(n_repetitions):\n", + " for top_n in top_n_pred: \n", + " for sh in shortners:\n", + " constructed_path = output_dir + model +\"_\"+ dataset +\"_\"+ str(n_rep) +\"_\"+ str(top_n) +\"_\"+ sh + \"unfiltered\" + \".json\"\n", + " if not os.path.exists(constructed_path):\n", + " continue\n", + " with open(constructed_path , \"r\") as f:\n", + " json_file = json.load(f)\n", + " key_name = model +\"_\"+ dataset +\"_\"+ str(top_n) +\"_\"+ sh\n", + " if key_name in results_unfiltered:\n", + " results_unfiltered[key_name][\"accuracy\"].append(json_file[\"accuracy\"])\n", + " results_unfiltered[key_name][\"macro avg\"].append(json_file[\"macro avg\"])\n", + " results_unfiltered[key_name][\"weighted avg\"].append(json_file[\"weighted avg\"])\n", + " if k == n_repetitions - 1:\n", + " results_unfiltered[key_name][\"macro avg summary\"] = calculate_avg_dict(results_unfiltered[key_name][\"macro avg\"])\n", + " results_unfiltered[key_name][\"macro avg summary\"]['accuracy'] = np.mean(results_unfiltered[key_name][\"accuracy\"])\n", + " results_unfiltered[key_name][\"weighted avg summary\"] = calculate_avg_dict(results_unfiltered[key_name][\"weighted avg\"])\n", + " results_unfiltered[key_name][\"weighted avg summary\"]['accuracy'] = np.mean(results_unfiltered[key_name][\"accuracy\"])\n", + " \n", + " if sh == '42_':\n", + " df_macro_avg_unfiltered.at[(model,'5_short_dim'),(top_n, 'precision')] = results_unfiltered[key_name][\"macro avg summary\"]['precision']\n", + " df_macro_avg_unfiltered.at[(model,'5_short_dim'),(top_n, 'recall')] = results_unfiltered[key_name][\"macro avg summary\"]['recall']\n", + " df_macro_avg_unfiltered.at[(model,'5_short_dim'),(top_n, 'f1-score')] = results_unfiltered[key_name][\"macro avg summary\"]['f1-score']\n", + " \n", + " df_weighted_avg_unfiltered.at[(model,'5_short_dim'),(top_n, 'precision')] = results_unfiltered[key_name][\"weighted avg summary\"]['precision']\n", + " df_weighted_avg_unfiltered.at[(model,'5_short_dim'),(top_n, 'recall')] = results_unfiltered[key_name][\"weighted avg summary\"]['recall']\n", + " df_weighted_avg_unfiltered.at[(model,'5_short_dim'),(top_n, 'f1-score')] = results_unfiltered[key_name][\"weighted avg summary\"]['f1-score']\n", + " else:\n", + " \n", + " #s = pd.Series(results[key_name][\"macro avg summary\"], name=key_name)\n", + " df_macro_avg_unfiltered.at[(model,dataset),(top_n, 'precision')] = results_unfiltered[key_name][\"macro avg summary\"]['precision']\n", + " df_macro_avg_unfiltered.at[(model,dataset),(top_n, 'recall')] = results_unfiltered[key_name][\"macro avg summary\"]['recall']\n", + " df_macro_avg_unfiltered.at[(model,dataset),(top_n, 'f1-score')] = results_unfiltered[key_name][\"macro avg summary\"]['f1-score']\n", + "\n", + " #df_macro_avg = df_macro_avg.append(s)\n", + " #s = pd.Series(results[key_name][\"weighted avg summary\"], name=key_name)\n", + " #df_weighted_avg = df_weighted_avg.append(s) \n", + "\n", + " df_weighted_avg_unfiltered.at[(model,dataset),(top_n, 'precision')] = results_unfiltered[key_name][\"weighted avg summary\"]['precision']\n", + " df_weighted_avg_unfiltered.at[(model,dataset),(top_n, 'recall')] = results_unfiltered[key_name][\"weighted avg summary\"]['recall']\n", + " df_weighted_avg_unfiltered.at[(model,dataset),(top_n, 'f1-score')] = results_unfiltered[key_name][\"weighted avg summary\"]['f1-score']\n", + " else:\n", + " results_unfiltered[key_name] = {\n", + " \"accuracy\":[json_file[\"accuracy\"]],\n", + " \"macro avg\":[json_file[\"macro avg\"]],\n", + " \"weighted avg\":[json_file[\"weighted avg\"]]\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "metadata": {}, + "outputs": [], + "source": [ + "results = dict()\n", + "df_macro_avg = pd.DataFrame(index=index, columns=columns)\n", + "df_weighted_avg = pd.DataFrame(index=index, columns=columns)\n", + "\n", + "for model in models:\n", + " for dataset in datasets:\n", + " for n_rep in range(n_repetitions):\n", + " for top_n in top_n_pred: \n", + " for sh in shortners: \n", + " constructed_path = output_dir + model +\"_\"+ dataset +\"_\"+ str(n_rep) +\"_\"+ str(top_n)\n", + " if not sh == '':\n", + " constructed_path = constructed_path + \"_\" + sh[:-1]\n", + " constructed_path = constructed_path + \".json\"\n", + " if not os.path.exists(constructed_path):\n", + " continue\n", + " with open(constructed_path , \"r\") as f:\n", + " json_file = json.load(f)\n", + " key_name = model +\"_\"+ dataset +\"_\"+ str(top_n) +\"_\"+ sh\n", + " if key_name in results:\n", + " results[key_name][\"accuracy\"].append(json_file[\"accuracy\"])\n", + " results[key_name][\"macro avg\"].append(json_file[\"macro avg\"])\n", + " results[key_name][\"weighted avg\"].append(json_file[\"weighted avg\"])\n", + " if k == n_repetitions - 1:\n", + " results[key_name][\"macro avg summary\"] = calculate_avg_dict(results[key_name][\"macro avg\"])\n", + " results[key_name][\"macro avg summary\"]['accuracy'] = np.mean(results[key_name][\"accuracy\"])\n", + " results[key_name][\"weighted avg summary\"] = calculate_avg_dict(results[key_name][\"weighted avg\"])\n", + " results[key_name][\"weighted avg summary\"]['accuracy'] = np.mean(results[key_name][\"accuracy\"])\n", + " \n", + " if sh == '42_':\n", + " df_macro_avg.at[(model,'5_short_dim'),(top_n, 'precision')] = results[key_name][\"macro avg summary\"]['precision']\n", + " df_macro_avg.at[(model,'5_short_dim'),(top_n, 'recall')] = results[key_name][\"macro avg summary\"]['recall']\n", + " df_macro_avg.at[(model,'5_short_dim'),(top_n, 'f1-score')] = results[key_name][\"macro avg summary\"]['f1-score']\n", + " \n", + " df_weighted_avg.at[(model,'5_short_dim'),(top_n, 'precision')] = results[key_name][\"weighted avg summary\"]['precision']\n", + " df_weighted_avg.at[(model,'5_short_dim'),(top_n, 'recall')] = results[key_name][\"weighted avg summary\"]['recall']\n", + " df_weighted_avg.at[(model,'5_short_dim'),(top_n, 'f1-score')] = results[key_name][\"weighted avg summary\"]['f1-score']\n", + " else:\n", + " \n", + " #s = pd.Series(results[key_name][\"macro avg summary\"], name=key_name)\n", + " df_macro_avg.at[(model,dataset),(top_n, 'precision')] = results[key_name][\"macro avg summary\"]['precision']\n", + " df_macro_avg.at[(model,dataset),(top_n, 'recall')] = results[key_name][\"macro avg summary\"]['recall']\n", + " df_macro_avg.at[(model,dataset),(top_n, 'f1-score')] = results[key_name][\"macro avg summary\"]['f1-score']\n", + "\n", + " #df_macro_avg = df_macro_avg.append(s)\n", + " #s = pd.Series(results[key_name][\"weighted avg summary\"], name=key_name)\n", + " #df_weighted_avg = df_weighted_avg.append(s) \n", + "\n", + " df_weighted_avg.at[(model,dataset),(top_n, 'precision')] = results[key_name][\"weighted avg summary\"]['precision']\n", + " df_weighted_avg.at[(model,dataset),(top_n, 'recall')] = results[key_name][\"weighted avg summary\"]['recall']\n", + " df_weighted_avg.at[(model,dataset),(top_n, 'f1-score')] = results[key_name][\"weighted avg summary\"]['f1-score']\n", + "\n", + " \n", + " \n", + " \n", + " else:\n", + " results[key_name] = {\n", + " \"accuracy\":[json_file[\"accuracy\"]],\n", + " \"macro avg\":[json_file[\"macro avg\"]],\n", + " \"weighted avg\":[json_file[\"weighted avg\"]]\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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top123
statsprecisionrecallf1-scoreprecisionrecallf1-scoreprecisionrecallf1-score
modelsubtype
load_m11_complete5NaNNaNNaNNaNNaNNaNNaNNaN
2_cf_cr_optionalNaNNaNNaNNaNNaNNaNNaNNaNNaN
3_cp_cf_cr_optionalNaNNaNNaNNaNNaNNaNNaNNaNNaN
4_complete_without_return_expressionsNaNNaNNaNNaNNaNNaNNaNNaNNaN
5_short_dimNaNNaNNaNNaNNaNNaNNaNNaNNaN
load_m21_completeNaNNaNNaNNaNNaNNaNNaNNaNNaN
2_cf_cr_optionalNaNNaNNaNNaNNaNNaNNaNNaNNaN
3_cp_cf_cr_optionalNaNNaNNaNNaNNaNNaNNaNNaNNaN
4_complete_without_return_expressionsNaNNaNNaNNaNNaNNaNNaNNaNNaN
5_short_dimNaNNaNNaNNaNNaNNaNNaNNaNNaN
load_m31_completeNaNNaNNaNNaNNaNNaNNaNNaNNaN
2_cf_cr_optionalNaNNaNNaNNaNNaNNaNNaNNaNNaN
3_cp_cf_cr_optionalNaNNaNNaNNaNNaNNaNNaNNaNNaN
4_complete_without_return_expressionsNaNNaNNaNNaNNaNNaNNaNNaNNaN
5_short_dimNaNNaNNaNNaNNaNNaNNaNNaNNaN
\n", + "
" + ], + "text/plain": [ + "top 1 \\\n", + "stats precision recall f1-score \n", + "model subtype \n", + "load_m1 1_complete 5 NaN NaN \n", + " 2_cf_cr_optional NaN NaN NaN \n", + " 3_cp_cf_cr_optional NaN NaN NaN \n", + " 4_complete_without_return_expressions NaN NaN NaN \n", + " 5_short_dim NaN NaN NaN \n", + "load_m2 1_complete NaN NaN NaN \n", + " 2_cf_cr_optional NaN NaN NaN \n", + " 3_cp_cf_cr_optional NaN NaN NaN \n", + " 4_complete_without_return_expressions NaN NaN NaN \n", + " 5_short_dim NaN NaN NaN \n", + "load_m3 1_complete NaN NaN NaN \n", + " 2_cf_cr_optional NaN NaN NaN \n", + " 3_cp_cf_cr_optional NaN NaN NaN \n", + " 4_complete_without_return_expressions NaN NaN NaN \n", + " 5_short_dim NaN NaN NaN \n", + "\n", + "top 2 \\\n", + "stats precision recall f1-score \n", + "model subtype \n", + "load_m1 1_complete NaN NaN NaN \n", + " 2_cf_cr_optional NaN NaN NaN \n", + " 3_cp_cf_cr_optional NaN NaN NaN \n", + " 4_complete_without_return_expressions NaN NaN NaN \n", + " 5_short_dim NaN NaN NaN \n", + "load_m2 1_complete NaN NaN NaN \n", + " 2_cf_cr_optional NaN NaN NaN \n", + " 3_cp_cf_cr_optional NaN NaN NaN \n", + " 4_complete_without_return_expressions NaN NaN NaN \n", + " 5_short_dim NaN NaN NaN \n", + "load_m3 1_complete NaN NaN NaN \n", + " 2_cf_cr_optional NaN NaN NaN \n", + " 3_cp_cf_cr_optional NaN NaN NaN \n", + " 4_complete_without_return_expressions NaN NaN NaN \n", + " 5_short_dim NaN NaN NaN \n", + "\n", + "top 3 \n", + "stats precision recall f1-score \n", + "model subtype \n", + "load_m1 1_complete NaN NaN NaN \n", + " 2_cf_cr_optional NaN NaN NaN \n", + " 3_cp_cf_cr_optional NaN NaN NaN \n", + " 4_complete_without_return_expressions NaN NaN NaN \n", + " 5_short_dim NaN NaN NaN \n", + "load_m2 1_complete NaN NaN NaN \n", + " 2_cf_cr_optional NaN NaN NaN \n", + " 3_cp_cf_cr_optional NaN NaN NaN \n", + " 4_complete_without_return_expressions NaN NaN NaN \n", + " 5_short_dim NaN NaN NaN \n", + "load_m3 1_complete NaN NaN NaN \n", + " 2_cf_cr_optional NaN NaN NaN \n", + " 3_cp_cf_cr_optional NaN NaN NaN \n", + " 4_complete_without_return_expressions NaN NaN NaN \n", + " 5_short_dim NaN NaN NaN " + ] + }, + "execution_count": 256, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "index\n", + "df = pd.DataFrame(index=index, columns=columns)\n", + "df.at[('load_m1', '1_complete'), (1,'precision')] = 5\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Results:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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top123
statsprecisionrecallf1-scoreprecisionrecallf1-scoreprecisionrecallf1-score
modelsubtype
load_m11_complete0.2983250.3040490.2834970.4268560.4010880.3921620.5089880.461460.462377
2_cf_cr_optional0.2064360.2187090.1906750.3512780.2978060.2916230.4633840.3686550.37926
3_cp_cf_cr_optional0.1996650.1793530.1649040.3465330.2560950.2608220.4594870.3234210.343976
4_complete_without_return_expressions0.2924870.3028250.2820420.4319640.4042180.3975690.5257530.4727110.474781
5_short_dim0.3697570.3659650.3495280.502420.4636430.461010.5921110.5306960.537645
load_m21_complete0.06340460.06472740.05908480.1201670.1065650.1046150.166170.1396160.14094
2_cf_cr_optional0.02592310.02948710.02493230.06334610.0518670.04993380.1071080.08126820.0832503
3_cp_cf_cr_optional0.03218870.03002530.02638880.07341140.0500090.04984510.1183810.07800580.0818357
4_complete_without_return_expressions0.06412730.06411660.05992630.1244320.1085770.1078970.173950.1475510.150411
5_short_dim0.06540120.06801290.06254390.1186150.1058430.1033650.165260.1406680.141591
load_m31_complete0.6655080.6365840.6246840.7874860.7265080.7327980.8493990.7746270.789445
2_cf_cr_optional0.4559830.371620.3545310.6161310.4590020.4733520.7124260.5243320.554588
3_cp_cf_cr_optional0.5385620.3867290.3851110.7075830.4774440.5057690.7937980.5357170.579364
4_complete_without_return_expressions0.6076850.5873510.5720420.7484780.6923360.6952460.8182450.7438620.75659
5_short_dim0.6663960.63710.626870.7923770.7274160.7347980.8476140.7702720.78594
\n", + "
" + ], + "text/plain": [ + "top 1 \\\n", + "stats precision recall \n", + "model subtype \n", + "load_m1 1_complete 0.298325 0.304049 \n", + " 2_cf_cr_optional 0.206436 0.218709 \n", + " 3_cp_cf_cr_optional 0.199665 0.179353 \n", + " 4_complete_without_return_expressions 0.292487 0.302825 \n", + " 5_short_dim 0.369757 0.365965 \n", + "load_m2 1_complete 0.0634046 0.0647274 \n", + " 2_cf_cr_optional 0.0259231 0.0294871 \n", + " 3_cp_cf_cr_optional 0.0321887 0.0300253 \n", + " 4_complete_without_return_expressions 0.0641273 0.0641166 \n", + " 5_short_dim 0.0654012 0.0680129 \n", + "load_m3 1_complete 0.665508 0.636584 \n", + " 2_cf_cr_optional 0.455983 0.37162 \n", + " 3_cp_cf_cr_optional 0.538562 0.386729 \n", + " 4_complete_without_return_expressions 0.607685 0.587351 \n", + " 5_short_dim 0.666396 0.6371 \n", + "\n", + "top 2 \\\n", + "stats f1-score precision recall \n", + "model subtype \n", + "load_m1 1_complete 0.283497 0.426856 0.401088 \n", + " 2_cf_cr_optional 0.190675 0.351278 0.297806 \n", + " 3_cp_cf_cr_optional 0.164904 0.346533 0.256095 \n", + " 4_complete_without_return_expressions 0.282042 0.431964 0.404218 \n", + " 5_short_dim 0.349528 0.50242 0.463643 \n", + "load_m2 1_complete 0.0590848 0.120167 0.106565 \n", + " 2_cf_cr_optional 0.0249323 0.0633461 0.051867 \n", + " 3_cp_cf_cr_optional 0.0263888 0.0734114 0.050009 \n", + " 4_complete_without_return_expressions 0.0599263 0.124432 0.108577 \n", + " 5_short_dim 0.0625439 0.118615 0.105843 \n", + "load_m3 1_complete 0.624684 0.787486 0.726508 \n", + " 2_cf_cr_optional 0.354531 0.616131 0.459002 \n", + " 3_cp_cf_cr_optional 0.385111 0.707583 0.477444 \n", + " 4_complete_without_return_expressions 0.572042 0.748478 0.692336 \n", + " 5_short_dim 0.62687 0.792377 0.727416 \n", + "\n", + "top 3 \\\n", + "stats f1-score precision recall \n", + "model subtype \n", + "load_m1 1_complete 0.392162 0.508988 0.46146 \n", + " 2_cf_cr_optional 0.291623 0.463384 0.368655 \n", + " 3_cp_cf_cr_optional 0.260822 0.459487 0.323421 \n", + " 4_complete_without_return_expressions 0.397569 0.525753 0.472711 \n", + " 5_short_dim 0.46101 0.592111 0.530696 \n", + "load_m2 1_complete 0.104615 0.16617 0.139616 \n", + " 2_cf_cr_optional 0.0499338 0.107108 0.0812682 \n", + " 3_cp_cf_cr_optional 0.0498451 0.118381 0.0780058 \n", + " 4_complete_without_return_expressions 0.107897 0.17395 0.147551 \n", + " 5_short_dim 0.103365 0.16526 0.140668 \n", + "load_m3 1_complete 0.732798 0.849399 0.774627 \n", + " 2_cf_cr_optional 0.473352 0.712426 0.524332 \n", + " 3_cp_cf_cr_optional 0.505769 0.793798 0.535717 \n", + " 4_complete_without_return_expressions 0.695246 0.818245 0.743862 \n", + " 5_short_dim 0.734798 0.847614 0.770272 \n", + "\n", + "top \n", + "stats f1-score \n", + "model subtype \n", + "load_m1 1_complete 0.462377 \n", + " 2_cf_cr_optional 0.37926 \n", + " 3_cp_cf_cr_optional 0.343976 \n", + " 4_complete_without_return_expressions 0.474781 \n", + " 5_short_dim 0.537645 \n", + "load_m2 1_complete 0.14094 \n", + " 2_cf_cr_optional 0.0832503 \n", + " 3_cp_cf_cr_optional 0.0818357 \n", + " 4_complete_without_return_expressions 0.150411 \n", + " 5_short_dim 0.141591 \n", + "load_m3 1_complete 0.789445 \n", + " 2_cf_cr_optional 0.554588 \n", + " 3_cp_cf_cr_optional 0.579364 \n", + " 4_complete_without_return_expressions 0.75659 \n", + " 5_short_dim 0.78594 " + ] + }, + "execution_count": 257, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_macro_avg" + ] + }, + { + "cell_type": "code", + "execution_count": 258, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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top123
statsprecisionrecallf1-scoreprecisionrecallf1-scoreprecisionrecallf1-score
modelsubtype
load_m11_complete0.3633760.3707280.3501490.4683060.4526360.4418160.5301350.5015420.497225
2_cf_cr_optional0.2550810.2705850.2435330.3529480.3350450.3199610.4316840.3954660.390194
3_cp_cf_cr_optional0.2440760.2384460.2206110.331680.2966070.2888760.4164430.3527880.355463
4_complete_without_return_expressions0.3608380.3762620.3534960.473910.4650320.452820.5509880.5230680.518248
5_short_dim0.4452780.4464780.4296340.5479910.5261110.5196950.6198440.5845810.584756
load_m21_complete0.08821480.09360730.08459290.1417750.1357420.1297450.1876020.1706280.167992
2_cf_cr_optional0.03491410.04045020.03472740.06208840.0599460.05588380.09805420.08448680.0829587
3_cp_cf_cr_optional0.04287490.04375560.0381940.0728060.06103530.05812210.1122360.08666550.086648
4_complete_without_return_expressions0.09382850.09791650.0905190.1468620.1365490.1326590.1914710.1714410.171333
5_short_dim0.08968840.09612630.08732150.1405290.135030.1288960.1848190.1708050.167486
load_m31_complete0.7389810.7196620.7104870.8228410.7892590.7900840.8691590.8286240.834943
2_cf_cr_optional0.5375190.4592220.4482790.6402660.5359930.5413770.7148520.5969140.611998
3_cp_cf_cr_optional0.6248580.5099610.5100540.7193050.5810810.595820.7847390.633890.657176
4_complete_without_return_expressions0.6812760.6671160.6547360.7839680.7507860.750040.83740.7940590.799302
5_short_dim0.7308740.7137870.7049220.8194320.784770.7864420.8643040.8220330.828505
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top123
statsprecisionrecallf1-scoreprecisionrecallf1-scoreprecisionrecallf1-score
modelsubtype
load_m11_complete0.5098310.5767380.5189560.6592830.7050160.6550960.7315360.7725610.729611
2_cf_cr_optional0.404290.4779530.4147120.596390.6213930.557920.682550.7015940.646866
3_cp_cf_cr_optional0.4127910.4810980.4161350.6022540.6219090.5571940.6870340.6977560.640979
4_complete_without_return_expressions0.5120730.5874480.5302810.675380.7183640.6710940.7484830.7821290.741918
5_short_dim0.5430450.5988270.5467890.6927840.727380.6838210.7660930.7934680.756833
load_m21_complete0.340030.4521420.3718980.5081020.5906760.5075260.5837080.6661940.590353
2_cf_cr_optional0.2519730.3680310.2867910.4122310.5180980.4162140.5251550.6000820.499896
3_cp_cf_cr_optional0.2703090.3797090.3005630.4308510.5257770.4252190.5413180.6054960.506533
4_complete_without_return_expressions0.3445520.4595440.3788750.5115180.5936490.5100670.5848430.6681430.592486
5_short_dim0.3294810.4488940.3671350.5047660.5886090.503860.5799390.6634970.587155
load_m31_complete0.7245630.731770.7111390.8347850.837290.8217390.882110.8822940.869586
2_cf_cr_optional0.5707570.5723950.5335620.7116240.7086280.6744010.7823950.7789960.749136
3_cp_cf_cr_optional0.588910.5822920.5461960.7315110.716020.6850380.8001580.7843460.757963
4_complete_without_return_expressions0.6982790.7155880.6912690.8154430.824730.8059870.867790.871880.85659
5_short_dim0.7181380.7305890.7111170.8328120.8353220.8202130.879640.8797740.867319
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" + ], + "text/plain": [ + "top 1 \\\n", + "stats precision recall f1-score \n", + "model subtype \n", + "load_m1 1_complete 0.509831 0.576738 0.518956 \n", + " 2_cf_cr_optional 0.40429 0.477953 0.414712 \n", + " 3_cp_cf_cr_optional 0.412791 0.481098 0.416135 \n", + " 4_complete_without_return_expressions 0.512073 0.587448 0.530281 \n", + " 5_short_dim 0.543045 0.598827 0.546789 \n", + "load_m2 1_complete 0.34003 0.452142 0.371898 \n", + " 2_cf_cr_optional 0.251973 0.368031 0.286791 \n", + " 3_cp_cf_cr_optional 0.270309 0.379709 0.300563 \n", + " 4_complete_without_return_expressions 0.344552 0.459544 0.378875 \n", + " 5_short_dim 0.329481 0.448894 0.367135 \n", + "load_m3 1_complete 0.724563 0.73177 0.711139 \n", + " 2_cf_cr_optional 0.570757 0.572395 0.533562 \n", + " 3_cp_cf_cr_optional 0.58891 0.582292 0.546196 \n", + " 4_complete_without_return_expressions 0.698279 0.715588 0.691269 \n", + " 5_short_dim 0.718138 0.730589 0.711117 \n", + "\n", + "top 2 \\\n", + "stats precision recall f1-score \n", + "model subtype \n", + "load_m1 1_complete 0.659283 0.705016 0.655096 \n", + " 2_cf_cr_optional 0.59639 0.621393 0.55792 \n", + " 3_cp_cf_cr_optional 0.602254 0.621909 0.557194 \n", + " 4_complete_without_return_expressions 0.67538 0.718364 0.671094 \n", + " 5_short_dim 0.692784 0.72738 0.683821 \n", + "load_m2 1_complete 0.508102 0.590676 0.507526 \n", + " 2_cf_cr_optional 0.412231 0.518098 0.416214 \n", + " 3_cp_cf_cr_optional 0.430851 0.525777 0.425219 \n", + " 4_complete_without_return_expressions 0.511518 0.593649 0.510067 \n", + " 5_short_dim 0.504766 0.588609 0.50386 \n", + "load_m3 1_complete 0.834785 0.83729 0.821739 \n", + " 2_cf_cr_optional 0.711624 0.708628 0.674401 \n", + " 3_cp_cf_cr_optional 0.731511 0.71602 0.685038 \n", + " 4_complete_without_return_expressions 0.815443 0.82473 0.805987 \n", + " 5_short_dim 0.832812 0.835322 0.820213 \n", + "\n", + "top 3 \n", + "stats precision recall f1-score \n", + "model subtype \n", + "load_m1 1_complete 0.731536 0.772561 0.729611 \n", + " 2_cf_cr_optional 0.68255 0.701594 0.646866 \n", + " 3_cp_cf_cr_optional 0.687034 0.697756 0.640979 \n", + " 4_complete_without_return_expressions 0.748483 0.782129 0.741918 \n", + " 5_short_dim 0.766093 0.793468 0.756833 \n", + "load_m2 1_complete 0.583708 0.666194 0.590353 \n", + " 2_cf_cr_optional 0.525155 0.600082 0.499896 \n", + " 3_cp_cf_cr_optional 0.541318 0.605496 0.506533 \n", + " 4_complete_without_return_expressions 0.584843 0.668143 0.592486 \n", + " 5_short_dim 0.579939 0.663497 0.587155 \n", + "load_m3 1_complete 0.88211 0.882294 0.869586 \n", + " 2_cf_cr_optional 0.782395 0.778996 0.749136 \n", + " 3_cp_cf_cr_optional 0.800158 0.784346 0.757963 \n", + " 4_complete_without_return_expressions 0.86779 0.87188 0.85659 \n", + " 5_short_dim 0.87964 0.879774 0.867319 " + ] + }, + "execution_count": 259, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_weighted_avg" + ] + }, + { + "cell_type": "code", + "execution_count": 260, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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top123
statsprecisionrecallf1-scoreprecisionrecallf1-scoreprecisionrecallf1-score
modelsubtype
load_m11_complete0.6032940.6770660.615530.7171590.7665850.7189730.7684180.8124530.773022
2_cf_cr_optional0.5143760.6200080.5415540.6564980.6965680.6353330.7266380.7565330.708718
3_cp_cf_cr_optional0.5286330.6282210.5492180.6606660.7008990.6385380.7290960.7555170.705464
4_complete_without_return_expressions0.6015110.6858440.6245670.7277020.7747550.7301950.7819230.8202170.783873
5_short_dim0.6385040.7031960.6472460.7477620.7856990.7447160.8005310.8323450.799612
load_m21_complete0.411980.5466770.4536440.5521720.6384430.555290.6169270.7000470.627548
2_cf_cr_optional0.3433990.5095060.3992980.4426880.5715920.4651920.5443440.635560.537012
3_cp_cf_cr_optional0.3657590.5271260.4184190.4568880.5741110.4699850.5651560.6409760.543573
4_complete_without_return_expressions0.4223860.559220.4661860.557780.6449420.5620580.6218920.7053890.633951
5_short_dim0.4040730.5465790.4514280.5466710.6375180.5534790.6107720.6982660.625081
load_m31_complete0.8170210.8320350.8114970.8837040.8917270.8785060.9136610.9185760.908634
2_cf_cr_optional0.6932110.6997940.6577370.7846580.7828210.7557150.8345020.8309470.810852
3_cp_cf_cr_optional0.7074570.7118910.67390.7975930.7922050.7683110.8445730.8377890.819555
4_complete_without_return_expressions0.7909050.8126350.7890480.8663070.8800930.8640110.9004430.9085350.896118
5_short_dim0.810060.8275160.8085710.8805690.890150.8772430.9118030.9161250.906359
\n", + "
" + ], + "text/plain": [ + "top 1 \\\n", + "stats precision recall f1-score \n", + "model subtype \n", + "load_m1 1_complete 0.603294 0.677066 0.61553 \n", + " 2_cf_cr_optional 0.514376 0.620008 0.541554 \n", + " 3_cp_cf_cr_optional 0.528633 0.628221 0.549218 \n", + " 4_complete_without_return_expressions 0.601511 0.685844 0.624567 \n", + " 5_short_dim 0.638504 0.703196 0.647246 \n", + "load_m2 1_complete 0.41198 0.546677 0.453644 \n", + " 2_cf_cr_optional 0.343399 0.509506 0.399298 \n", + " 3_cp_cf_cr_optional 0.365759 0.527126 0.418419 \n", + " 4_complete_without_return_expressions 0.422386 0.55922 0.466186 \n", + " 5_short_dim 0.404073 0.546579 0.451428 \n", + "load_m3 1_complete 0.817021 0.832035 0.811497 \n", + " 2_cf_cr_optional 0.693211 0.699794 0.657737 \n", + " 3_cp_cf_cr_optional 0.707457 0.711891 0.6739 \n", + " 4_complete_without_return_expressions 0.790905 0.812635 0.789048 \n", + " 5_short_dim 0.81006 0.827516 0.808571 \n", + "\n", + "top 2 \\\n", + "stats precision recall f1-score \n", + "model subtype \n", + "load_m1 1_complete 0.717159 0.766585 0.718973 \n", + " 2_cf_cr_optional 0.656498 0.696568 0.635333 \n", + " 3_cp_cf_cr_optional 0.660666 0.700899 0.638538 \n", + " 4_complete_without_return_expressions 0.727702 0.774755 0.730195 \n", + " 5_short_dim 0.747762 0.785699 0.744716 \n", + "load_m2 1_complete 0.552172 0.638443 0.55529 \n", + " 2_cf_cr_optional 0.442688 0.571592 0.465192 \n", + " 3_cp_cf_cr_optional 0.456888 0.574111 0.469985 \n", + " 4_complete_without_return_expressions 0.55778 0.644942 0.562058 \n", + " 5_short_dim 0.546671 0.637518 0.553479 \n", + "load_m3 1_complete 0.883704 0.891727 0.878506 \n", + " 2_cf_cr_optional 0.784658 0.782821 0.755715 \n", + " 3_cp_cf_cr_optional 0.797593 0.792205 0.768311 \n", + " 4_complete_without_return_expressions 0.866307 0.880093 0.864011 \n", + " 5_short_dim 0.880569 0.89015 0.877243 \n", + "\n", + "top 3 \n", + "stats precision recall f1-score \n", + "model subtype \n", + "load_m1 1_complete 0.768418 0.812453 0.773022 \n", + " 2_cf_cr_optional 0.726638 0.756533 0.708718 \n", + " 3_cp_cf_cr_optional 0.729096 0.755517 0.705464 \n", + " 4_complete_without_return_expressions 0.781923 0.820217 0.783873 \n", + " 5_short_dim 0.800531 0.832345 0.799612 \n", + "load_m2 1_complete 0.616927 0.700047 0.627548 \n", + " 2_cf_cr_optional 0.544344 0.63556 0.537012 \n", + " 3_cp_cf_cr_optional 0.565156 0.640976 0.543573 \n", + " 4_complete_without_return_expressions 0.621892 0.705389 0.633951 \n", + " 5_short_dim 0.610772 0.698266 0.625081 \n", + "load_m3 1_complete 0.913661 0.918576 0.908634 \n", + " 2_cf_cr_optional 0.834502 0.830947 0.810852 \n", + " 3_cp_cf_cr_optional 0.844573 0.837789 0.819555 \n", + " 4_complete_without_return_expressions 0.900443 0.908535 0.896118 \n", + " 5_short_dim 0.911803 0.916125 0.906359 " + ] + }, + "execution_count": 260, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_weighted_avg_unfiltered" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Top Results:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Precision:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}